The use of spinal manipulation to treat an acute on field athletic injury: a case report.
Bibliographic record
Abstract
This case describes the utilization of spinal manipulative therapy for an acute athletic injury during a Taekwondo competition. During the tournament, an athlete had a sudden, non-traumatic, ballistic movement of the cervical spine. This resulted in the patient having a locked cervical spine with limited active motion in all directions. The attending chiropractor assessed the athlete, and deemed manipulation was appropriate. After the manipulation, the athlete's range of motion was returned and was able to finish the match. Spinal manipulation has multiple positive outcomes for an athlete with an acute injury including the increase of range of motion, decrease in pain and the relaxation of hypertonic muscles. However, there should be some caution when utilizing manipulation during an event. In the article the authors propose four criteria that should be met before utilizing manipulation for an acute, in competition, athletic injury. These include the lack of red flags, limited time for the intervention, preexisting doctor-patient relationship and the athlete has experience receiving spinal manipulation. Clinicians should be aware that manipulation may be an effective tool to treat an acute in competition athletic injury. The criteria set out in the article may help a practitioner decide if manipulation is a good option for them.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.017 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".